# Market Value of Differentially-Private Smart Meter Data

arXiv: https://arxiv.org/abs/2104.09898 · HTML: https://mlsanity.com/paper/2104.09898/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2021.

## Built on by (most cited first, 2 of 2)

- [On the 1-Wasserstein Distance between Location-Scale Distributions and the Effect of Differential Privacy](https://mlsanity.com/paper/2304.14869/) (2023)
- [Privacy-preserving Federated Learning for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2111.09248/) (2021)

## Abstract (arXiv)

This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on the forecasting error and an optimal procurement problem in day-ahead and balancing markets to assess the market value of the privacy-utility trade-off. The framework demonstrates that when the load profile of a consumer group differs from the system average, which is quantified using the Kullback-Leibler divergence, there is significant value in sharing smart meter data while retaining individual consumer privacy.